Gespeichert in:
| Beteiligte Personen: | , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2009
|
| Schriftenreihe: | Studies in computational intelligence
178 |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018677109&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Literaturangaben |
| Umfang: | XVII, 251 S. Ill., graph. Darst. 24 cm |
| ISBN: | 9783540921721 |
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| 245 | 1 | 0 | |a Metaheuristic clustering |c Swagatam Das ; Ajith Abraham ; Amit Konar |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2009 | |
| 300 | |a XVII, 251 S. |b Ill., graph. Darst. |c 24 cm | ||
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| 490 | 1 | |a Studies in computational intelligence |v 178 | |
| 500 | |a Literaturangaben | ||
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| 650 | 4 | |a Cluster-Analyse - Metaheuristik - Evolutionärer Algorithmus | |
| 650 | 4 | |a Exploration de données (Informatique) | |
| 650 | 4 | |a Programmation heuristique | |
| 650 | 4 | |a Cluster analysis | |
| 650 | 4 | |a Data mining | |
| 650 | 4 | |a Heuristic programming | |
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Datensatz im Suchindex
| _version_ | 1819287950400159744 |
|---|---|
| adam_text | CONTENTS 1 METAHEURISTIC PATTERN CLUSTERING - AN OVERVIEW 1 1.1
INTRODUCTION 1 1.2 THE CLUSTERING PROBLEM 6 1.2.1 BASIC DEFINITIONS 6
1.2.2 PROXIMITY MEASURES 8 1.2.3 CLUSTERING VALIDITY INDICES 9 1.2.3.1
THE DAVIS-BOULDIN (DB) INDEX 9 .2.3.2 THE DUNN AND DUNN LIKE INDICES 10
.2.3.3 S_DBW VALIDITY INDEX 10 .2.3.4 PARTITION COEFFICIENT 11 .2.3.5
CLASSIFICATION ENTROPY 12 .2.3.6 XIE-BENI INDEX 12 .2.3.7 THE PS MEASURE
12 .2.3.8 THE PBMF INDEX 13 .2.3.9 THECS MEASURE 13 1.3 THE CLASSICAL
CLUSTERING ALGORITHMS 14 1.3.1 HIERARCHICAL CLUSTERING ALGORITHMS 14
1.3.2 PARTITIONAL CLUSTERING ALGORITHMS 16 1.3.2.1 THE K-MEANS ALGORITHM
18 .3.2.2 THE K-MEDOIDS ALGORITHM 19 .3.2.3 THE FUZZY C-MEANS ALGORITHM
19 .3.2.4 THE EXPECTATION-MAXIMIZATION ALGORITHM 20 .3.2.5 THE
K-HARMONIC MEANS ALGORITHM 21 1.3.3 DENSITY-BASED CLUSTERING ALGORITHMS
22 1.3.4 GRID-BASED CLUSTERING ALGORITHMS 23 1.3.5 A COMPARATIVE VIEW OF
THE TRADITIONAL CLUSTERING ALGORITHMS 23 1.4 POPULATION BASED
OPTIMIZATION TECHNIQUES 26 1.4.1 OPTIMIZATION ALGORITHMS 26 1.4.2 THE
EVOLUTIONARY COMPUTING (EC) FAMILY 28 1.4.3 THE EVOLUTIONARY ALGORITHMS
29 1.4.3.1 EVOLUTIONARY STRATEGIES (ESS) 30 1.4.3.2 EVOLUTIONARY
PROGRAMMING (EP) 30 1.4.3.3 GENETIC ALGORITHMS (GAS) 31 1.4.3.4 GENETIC
PROGRAMMING (GPS) 33 1.4. BIBLIOGRAFISCHE INFORMATIONEN
HTTP://D-NB.INFO/991139844 DIGITALISIERT DURCH XIV CONTENTS 1.4.4.1 THE
PARTICLE SWARM OPTIMIZATION (PSO) 34 1.4.4.2 THE ANT COLONY OPTIMIZATION
(ACO) 35 1.4.5 EVOLUTIONARY COMPUTING (EC) TECHNIQUES IN PATTERN
CLUSTERING 36 1.5 CLUSTERING METHODS BASED ON EVOLUTIONARY ALGORITHMS 36
1.5.1 THE GA-BASED PARTITIONAL CLUSTERING ALGORITHMS - EARLIER
APPROACHES 37 1.5.2 CLUSTERING ALGORITHMS BASED ON ES, EP, AND GP 38 1.6
CLUSTERING USING SWARM INTELLIGENCE ALGORITHMS 39 1.6.1 THE ANT COLONY
BASED CLUSTERING ALGORITHMS 39 1.6.2 THE PSO-BASED CLUSTERING ALGORITHMS
40 1.7 AUTOMATIC CLUSTERING: EVOLUTIONARY VS. CLASSICAL APPROACHES 42
1.7.2 GENETIC CLUSTERING WITH UNKNOWN NUMBER OF CLUSTERS K (GCUK)
ALGORITHM 43 1.7.3 THE FVGA ALGORITHM 44 1.7.4 THE DYNAMIC CLUSTERING
WITH PARTICLE SWARM OPTIMIZATION ALGORITHM 45 1.8 CLUSTERING WITH
EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION 45 1.8.1 MULTI-OBJECTIVE
OPTIMIZATION PROBLEM (MOP) 45 1.8.2 EVOLUTIONARY MULTI-OBJECTIVE
OPTIMIZATION (EMO) 46 1.8.3 CLUSTERING USING EMO ALGORITHMS (EMOAS) 48
1.9 INNOVATION AND RESEARCH: MAIN CONTRIBUTIONS OF THIS VOLUME 49 1.10
CONCLUSIONS 53 REFERENCES 53 2 DIFFERENTIAL EVOLUTION ALGORITHM:
FOUNDATIONS AND PERSPECTIVES 63 2. 1 INTRODUCTION 63 2.2 DIFFERENTIAL
EVOLUTION: A FIRST GLANCE 64 2.2.1 INITIALIZATION OF THE PARAMETER
VECTORS 64 2.2.2 MUTATION WITH DIFFERENTIA] OPERATORS 66 2.2. CONTENTS
XV 2.5.8 SELF-ADAPTIVE DIFFERENTIAL EVOLUTION (SADE) WITH STRATEGY
ADAPTATION 92 2.5.9 DE WITH NEIGHBORHOOD-BASED MUTATION 93 2.5.9.1 THE
DE/TARGET-TO-BEST/1 - A FEW DRAWBACKS 93 2.5.9.2 MOTIVATIONS FOR THE
NEIGHBORHOOD-BASED MUTATION 94 2.5.9.3 THE LOCAL AND GLOBAL
NEIGHBORHOOD-BASED MUTATIONS IN DE 95 2.5.9.4 CONTROL PARAMETERS IN DEGL
97 2.5.9.5 RUNTIME COMPLEXITY OF DEGL -A DISCUSSION 99 2.5.9.6
COMPARATIVE PERFORMANCE OF DEGL 102 2.6 HYBRIDIZATION OF DIFFERENTIAL
EVOLUTION WITH OTHER STOCHASTIC SEARCH TECHNIQUES 104 2.7 CONCLUSIONS
106 REFERENCES 107 3 MODELING AND ANALYSIS OF THE POPULATION-DYNAMICS OF
DIFFERENTIAL EVOLUTION ALGORITHM ILL 3.1 INTRODUCTION ILL 3.2 THE
MATHEMATICAL MODEL OF THE POPULATION-DYNAMICS IN DE 112 3.2.1
ASSUMPTIONS 113 3.2.2 MODELING DIFFERENT STEPS OF DE 114 3.3 A STATE
SPACE FORMULATION OF THE DE POPULATION 122 3.4 LYAPUNOV STABILITY
ANALYSIS OF THE DE POPULATION 124 3.5 COMPUTER SIMULATION RESULTS 129
3.6 CONCLUSIONS 131 APPENDIX 132 REFERENCES 1 33 4 AUTOMATIC HARD
CLUSTERING USING IMPROVED DIFFERENTIAL EVOLUTION ALGORITHM 137 4.1
INTRODUCTION 137 4.2 THE DE-BASED AUTOMATIC CLUSTERING ALGORITHM 138
4.2.1 VECTOR REPRESENTATION 138 4.2.2 DESIGNING THE FITNESS FUNCTION 140
4.2. XVI CONTENTS 4.4.1 IMAGE SEGMENTATION AS A CLUSTERING PROBLEM 162
4.4.2 EXPERIMENTAL DETAILS AND RESULTS 162 4.4.3 DISCUSSION ON IMAGE
SEGMENTATION RESULTS 165 4.5 CONCLUSIONS 172 APPENDIX: STATISTICAL TESTS
USED 172 REFERENCES 173 5 FUZZY CLUSTERING IN THE KERNEL-INDUCED FEATURE
SPACE USING DIFFERENTIAL EVOLUTION ALGORITHM 175 5.1 INTRODUCTION 175
5.2 THE KERNEL-INDUCED CLUSTERING 177 5.3 THE KERNEL-INDUCED CLUSTERING
TECHNIQUE WITH DEGL 181 5.3.1 KERNELIZATION OF THEXIE-BENI INDEX 181
5.3.2 SUMMARY OF THE INTEGRATED CLUSTERING APPROACH 183 5.4 EXPERIMENTAL
RESULTS 184 5.4.1 GENERAL COMPARISON WITH OTHER CLUSTERING ALGORITHMS
184 5.4.2 SCALABILITY OF THE DEGL-BASED CLUSTERING ALGORITHM 194 5.5
APPLICATION TO IMAGE PIXEL CLUSTERING 197 5.5.1 PARAMETRIC SETUP FOR THE
CONTESTANT ALGORITHMS 197 5.5.2 THE TEST-SUITE FOR COMPARISON 198 5.5.3
QUANTITATIVE VALIDATION OF CLUSTERING RESULTS 198 5.5.4 THE SIMULATION
STRATEGY 199 5.5.5 EXPERIMENTAL RESULTS 200 5.5.6 DISCUSSION ON THE
RESULTS 202 5.6 CONCLUSIONS 208 REFERENCES 208 6 CLUSTERING USING
MULTI-OBJECTIVE DIFFERENTIAL EVOLUTION ALGORITHMS 213 6.1 INTRODUCTION
213 6.2 MULTI-OBJECTIVE OPTIMIZATION USING DIFFERENTIAL EVOLUTION
ALGORITHM 215 6.2.1 THE PARETO DIFFERENTIAL EVOLUTION (PDE) 215 6.2.2
THE MULTI-OBJECTIVE DIFFERENTIAL EVOLUTION (MODE) 216 6.2. CONTENTS XVII
6.4.4 SIGNIFICANCE AND VALIDATION OF MICROARRAY DATA CLUSTERIG RESULTS
228 6.5 CONCLUSIONS 236 REFERENCES 237 7 CONCLUSIONS AND FUTURE RESEARCH
239 7.1 CLUSTER ANALYSIS USING METAHEURISTICS: A ROADMAP OF THIS VOLUME
239 7.2 POTENTIAL APPLICATION AREAS FOR CLUSTERING SCHEMES 241 7.3
FUTURE RESEARCH DIRECTIONS 242 REFERENCES 245 INDEX 249
|
| any_adam_object | 1 |
| author | Das, Swagatam Abraham, Ajith 1968- Konar, Amit |
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| classification_rvk | QH 234 |
| ctrlnum | (OCoLC)302080539 (DE-599)DNB991139844 |
| dewey-full | 519.53 006.3 |
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| dewey-raw | 519.53 006.3 |
| dewey-search | 519.53 006.3 |
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| discipline | Informatik Mathematik Wirtschaftswissenschaften |
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| id | DE-604.BV035818309 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T14:00:53Z |
| institution | BVB |
| isbn | 9783540921721 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-018677109 |
| oclc_num | 302080539 |
| open_access_boolean | |
| owner | DE-739 |
| owner_facet | DE-739 |
| physical | XVII, 251 S. Ill., graph. Darst. 24 cm |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spellingShingle | Das, Swagatam Abraham, Ajith 1968- Konar, Amit Metaheuristic clustering Studies in computational intelligence Classification automatique (Statistique) Cluster-Analyse - Metaheuristik - Evolutionärer Algorithmus Exploration de données (Informatique) Programmation heuristique Cluster analysis Data mining Heuristic programming Evolutionärer Algorithmus (DE-588)4366912-8 gnd Metaheuristik (DE-588)4820176-5 gnd Cluster-Analyse (DE-588)4070044-6 gnd |
| subject_GND | (DE-588)4366912-8 (DE-588)4820176-5 (DE-588)4070044-6 |
| title | Metaheuristic clustering |
| title_auth | Metaheuristic clustering |
| title_exact_search | Metaheuristic clustering |
| title_full | Metaheuristic clustering Swagatam Das ; Ajith Abraham ; Amit Konar |
| title_fullStr | Metaheuristic clustering Swagatam Das ; Ajith Abraham ; Amit Konar |
| title_full_unstemmed | Metaheuristic clustering Swagatam Das ; Ajith Abraham ; Amit Konar |
| title_short | Metaheuristic clustering |
| title_sort | metaheuristic clustering |
| topic | Classification automatique (Statistique) Cluster-Analyse - Metaheuristik - Evolutionärer Algorithmus Exploration de données (Informatique) Programmation heuristique Cluster analysis Data mining Heuristic programming Evolutionärer Algorithmus (DE-588)4366912-8 gnd Metaheuristik (DE-588)4820176-5 gnd Cluster-Analyse (DE-588)4070044-6 gnd |
| topic_facet | Classification automatique (Statistique) Cluster-Analyse - Metaheuristik - Evolutionärer Algorithmus Exploration de données (Informatique) Programmation heuristique Cluster analysis Data mining Heuristic programming Evolutionärer Algorithmus Metaheuristik Cluster-Analyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018677109&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT dasswagatam metaheuristicclustering AT abrahamajith metaheuristicclustering AT konaramit metaheuristicclustering |